46 citations · 68 across the 8 of their papers we have counts for
5 papers · 1 filter
Learning Large Causal Structures from Inverse Covariance Matrix via Sparse Matrix Decomposition
Shuyu Dong, Kento Uemura, Akito Fujii +4
Learning causal structures from observational data is a fundamental problem facing important computational challenges when the number of variables is large. In the context of linea…
Automated Data Augmentations for Graph Classification
Youzhi Luo, Michael McThrow, Wing Yee Au +4
Data augmentations are effective in improving the invariance of learning machines. We argue that the core challenge of data augmentations lies in designing data transformations tha…
Inter-domain Multi-relational Link Prediction
Luu Huu Phuc, Koh Takeuchi, Seiji Okajima +4
Multi-relational graph is a ubiquitous and important data structure, allowing flexible representation of multiple types of interactions and relations between entities. Similar to o…
Bermuda Triangles: GNNs Fail to Detect Simple Topological Structures
Arseny Tolmachev, Akira Sakai, Masaru Todoriki +1
Most graph neural network architectures work by message-passing node vector embeddings over the adjacency matrix, and it is assumed that they capture graph topology by doing that.…
Linear Tensor Projection Revealing Nonlinearity
Koji Maruhashi, Heewon Park, Rui Yamaguchi +1
Dimensionality reduction is an effective method for learning high-dimensional data, which can provide better understanding of decision boundaries in human-readable low-dimensional…